To address these issues, we propose a new feature extraction network that can be adapted to various object scales and image resolutions. We refer the proposed network as curvature-guided dynamic scale feature network (CDSFNet). The proposed network is composed of novel convolution layers, curvature-guided dynamic scale convolution (CDSConv), which select a suitable patch scale for each pixel to learn robust representation. This selection is achieved by computing the normal curvature of the image surface at several candidate scales and then analyzing the computed outputs via a classification network. The pixel-level patch scales estimated from the proposed network are dynamic with respect to textures, scale of objects, and epipolar geometry. Therefore, it trains more discriminative features than existing networks [1]}, [2]}, [3]}, [4]}, [5]} for accurate matching cost computation. Fig. REF  illustrates the dynamic pixel-level patch scales estimated from CDSConv where the window size is ranged from \(7 \times 7\)  to \(17 \times 17\) . As shown in the figure, CDSConv predicts the adaptive patch scales where the scale is small in thin structure or texture-rich regions and large in the texture-less regions. Moreover, the scale is dynamic according to image resolution. CDSConv generally produces small scale values in a low-resolution image and large scale values in a high-resolution image.
